class_docstr: Resolves group-by items for potentially ambiguous inputs that are specified in queries / filters.
docstr: Returns the spec that matches the spec_pattern associated with the filter in the given node.
  
  The input_str is a string representation of the input for use in generating suggestions for errors.
  
  e.g. if the query has a where filter like "{{ TimeDimension('metric_time') }} = '2020-01-01'", then to resolve
  the spec for TimeDimension('metric_time'), the resolution node would be a QueryGroupByItemResolutionNode() from
  the DAG used in the initialization of this resolver.
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code: def resolve_matching_item_for_filters(
      self,
      input_str: str,
      spec_pattern: SpecPattern,
      resolution_node: ResolutionDagSinkNode,
  ) -> GroupByItemResolution:
      """Returns the spec that matches the spec_pattern associated with the filter in the given node.
  
      The input_str is a string representation of the input for use in generating suggestions for errors.
  
      e.g. if the query has a where filter like "{{ TimeDimension('metric_time') }} = '2020-01-01'", then to resolve
      the spec for TimeDimension('metric_time'), the resolution node would be a QueryGroupByItemResolutionNode() from
      the DAG used in the initialization of this resolver.
      """
      suggestion_generator = QueryItemSuggestionGenerator(
          input_naming_scheme=ObjectBuilderNamingScheme(),
          input_str=input_str,
          candidate_filters=QueryItemSuggestionGenerator.GROUP_BY_ITEM_CANDIDATE_FILTERS,
      )
  
      push_down_visitor = _PushDownGroupByItemCandidatesVisitor(
          manifest_lookup=self._manifest_lookup,
          source_spec_patterns=(
              spec_pattern,
              BaseTimeGrainPattern(),
          ),
          suggestion_generator=suggestion_generator,
      )
  
      push_down_result: PushDownResult = resolution_node.accept(
          push_down_visitor
      )
  
      if push_down_result.candidate_set.num_candidates == 0:
          return GroupByItemResolution(
              spec=None,
              issue_set=push_down_result.issue_set,
          )
  
      if push_down_result.candidate_set.num_candidates > 1:
          return GroupByItemResolution(
              spec=None,
              issue_set=push_down_result.issue_set.add_issue(
                  AmbiguousGroupByItemIssue.from_parameters(
                      candidate_set=push_down_result.candidate_set,
                      query_resolution_path=MetricFlowQueryResolutionPath.from_path_item(
                          self._resolution_dag.sink_node
                      ),
                  )
              ),
          )
  
      return GroupByItemResolution(
          spec=push_down_result.candidate_set.specs[0],
          issue_set=push_down_result.issue_set,
      )
  
imports: from __future__ import annotations
  import logging
  from dataclasses import dataclass
  from typing import Optional, Tuple
  from dbt_semantic_interfaces.call_parameter_sets import TimeDimensionCallParameterSet
  from dbt_semantic_interfaces.naming.keywords import METRIC_TIME_ELEMENT_NAME
  from dbt_semantic_interfaces.references import TimeDimensionReference
  from dbt_semantic_interfaces.type_enums import TimeGranularity
  from metricflow.mf_logging.formatting import indent
  from metricflow.mf_logging.pretty_print import mf_pformat
  from metricflow.model.semantic_manifest_lookup import SemanticManifestLookup
  from metricflow.naming.object_builder_scheme import ObjectBuilderNamingScheme
  from metricflow.query.group_by_item.candidate_push_down.push_down_visitor import PushDownResult, _PushDownGroupByItemCandidatesVisitor
  from metricflow.query.group_by_item.resolution_dag.dag import GroupByItemResolutionDag, ResolutionDagSinkNode
  from metricflow.query.group_by_item.resolution_path import MetricFlowQueryResolutionPath
  from metricflow.query.issues.group_by_item_resolver.ambiguous_group_by_item import AmbiguousGroupByItemIssue
  from metricflow.query.issues.issues_base import MetricFlowQueryResolutionIssueSet
  from metricflow.query.suggestion_generator import QueryItemSuggestionGenerator
  from metricflow.specs.patterns.base_time_grain import BaseTimeGrainPattern
  from metricflow.specs.patterns.spec_pattern import SpecPattern
  from metricflow.specs.patterns.typed_patterns import TimeDimensionPattern
  from metricflow.specs.specs import LinkableInstanceSpec, LinkableSpecSet
label_desc: Use image node_id 3 for calling the GroupByItemResolver obj's underlying member method code with example usage: obj.resolve_matching_item_for_filters(input_str, spec_pattern, resolution_node) and returns: GroupByItemResolution, GroupByItemResolution, GroupByItemResolution